To star the Retail OS, we need User, Product Catalog in our repository then only we can process any DAG. Recommendation Forecasting Inventory Optimisation Step 0: Prerequisite Entity SKU Article

Approach 1 : Template based QA system Elements of BNLE Identifying different elements of the question and tagging elements as Nouns, verbs etc. Converting Nouns into Brain Tokens Disabiguation using context Using Auxilary verbs to identify the template Combining adpositions and Brain Tokens Combining with the template to create a Knowledge Question Using Knowledge Question create a dependency graph

Model Store Service versions the machine learning models including their ingredients like code, data, config,environment and to track ML metadata across the model lifecycle. Use Model Repository Service in order to: Make ML models reproducible Register, track, Manage, and version your trained, deployed, and retired models in a central repository that is organized and searchable. Store the metadata for your trained models, as well as their runtime dependencies so the deployment process is eased. Build automated pipelines that make continuous integration, delivery, and training of your production model possible. Compare models running in production (champion models) to freshly trained models (or challenger models) in the staging environment.

The Label Repository is a unique repository within TelOS, designed to store label metadata associated with a specific record_key. The primary responsibility of the Label Repository is to handle CRUD operations and search functionality. Labels stored in this repository can be generated through various sources, including human input, AutoML, or other processes. For instance, consider an image where the system has identified various types of fruits and labeled them accordingly. image source: https://www.superannotate.com/blog/guide-to-data-labeling The Label Repository is responsible for storing these labels at an atomic level, which includes: Record Key – Identifying what we are labeling. Label Type – Determining the type of labeling.

Step : write 10 diffirent kind of questions in plain english Write corresponding State Object Finalize on Generic State Object S, A --> S1 --> Stop Answer what is the length of Ganga? What is the population of India? length - attribute

<span style="color:blue"/>Reasoning without a template <span style="color:blue"/>Like a Reasoning but unlike any Reasoning Team : Pranav (intern), Sudhanshu, Naresh, Krishna Philosophy Compass over Map when doing reasoning! Objective

<span style="color:purple"/>You shall know an Entity by the company it keeps in the Knowledge Graph! Scope Brain Knowledge Repository 1.2 will offer inhertiance support as the key offering a long with amazing enahancments on top of 1.1 Inhertiance Support 1.1 Parent and Child Class Level Support 1.2 Taxonomy representation Feature enhancement 2.1 Aggregation : MIN, MAX, STANDARD_DEVIATION, Mean, Median 2.2 GroupBy : Resultset optimization

Like a Knowledge Graph. Unlike any Knowledge Graph. Reasoning first knowledge graph Knowledge = Enumerate all the options, example Rules of the Game. Constraints my forward path Learning = How do I move with the knowledge, which is a better option. Goodness of State, given the action, state & goal. where do I go? Reasoning = Using Learning + Knowledge in recursive path Factoid question are not answerable via KG 1.0 APIs You shall know an Entity by the company it keeps in the Knowledge Graph! Objective : The Reasoning Brain

Question/Answer by Dr Bhadresh Question : Is Wheat/Brown bread good? Answer: No | [reason ] : bread composition > carbos > gloc.... > Not Good Question : Is X Pasta is good? Answer Question Collection

Repository | Knowledge APIs Vertical Agnostic APIs for Knowledge Repository [TOC] Type of Knowledge APIs Knowledge Schema APIs – manage meta-data about the knowledge repository. Knowledge Identity APIs – manages the mapping between business and internal entity id’s. Brain Identity Service is the one who takes care of maintaining the BrainID across the system hence we decided to keep all the identity-related responsibilities with Brain Identity Service itself. We will not have a Knowledge Identity as a microservice. Knowledge Name APIs – manages the entity identity and name mapping across languages. Brain Name Service is the one who takes care of maintaining the names across the system hence we decided to keep all the name-related responsibilities with Brain Name Service itself. We will not have a Knowledge Name as a microservice.

Hydrocarbon Ontology Entities Attributes Dynamic Attributes Static attributes Attribute groups Dynamic attributes that belong to an attribute groups